Statistics for Exercise Science and Health with Microsoft Office Excel, Verma
Автор: Deshea Название: Introductory Statistics for the Health Sciences ISBN: 1466565330 ISBN-13(EAN): 9781466565333 Издательство: Taylor&Francis Рейтинг: Цена: 13779.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Introductory Statistics for the Health Sciences takes students on a journey to a wilderness where science explores the unknown, providing students with a strong, practical foundation in statistics. Using a color format throughout, the book contains engaging figures that illustrate real data sets from published research. Examples come from many areas of the health sciences, including medicine, nursing, pharmacy, dentistry, and physical therapy, but are understandable to students in any field. The book can be used in a first-semester course in a health sciences program or in a service course for undergraduate students who plan to enter a health sciences program.
The book begins by explaining the research context for statistics in the health sciences, which provides students with a framework for understanding why they need statistics as well as a foundation for the remainder of the text. It emphasizes kinds of variables and their relationships throughout, giving a substantive context for descriptive statistics, graphs, probability, inferential statistics, and interval estimation. The final chapter organizes the statistical procedures in a decision tree and leads students through a process of assessing research scenarios.
Web Resource The authors have partnered with William Howard Beasley, who created the illustrations in the book, to offer all of the data sets, graphs, and graphing code in an online data repository via GitHub. A dedicated website gives information about the data sets and the authors' electronic flashcards for iOS and Android devices. These flashcards help students learn new terms and concepts.
Автор: Abbott Martin Lee Название: Using Statistics in the Social and Health Sciences with SPSS ISBN: 1119121043 ISBN-13(EAN): 9781119121046 Издательство: Wiley Рейтинг: Цена: 16466.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the title, both SPSS and Excel are accompanied by the trademark symbol.
Описание: Presents an overview of the complex biological systems used within a global public health setting and features a focus on malaria analysis Bridging the gap between agent–based modeling and simulation (ABMS) and geographic information systems (GIS), Spatial Agent–Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology provides a useful introduction to the development of agent–based models (ABMs) by following a conceptual and biological core model of Anopheles gambiae for malaria epidemiology. Using spatial ABMs, the book includes mosquito (vector) control interventions and GIS as two example applications of ABMs, as well as a brief description of epidemiology modeling. In addition, the authors discuss how to most effectively integrate spatial ABMs with a GIS. The book concludes with a combination of knowledge from entomological, epidemiological, simulation–based, and geo–spatial domains in order to identify and analyze relationships between various transmission variables of the disease. Spatial Agent–Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology also features: Location–specific mosquito abundance maps that play an important role in malaria control activities by guiding future resource allocation for malaria control and identifying hotspots for further investigation Discussions on the best modeling practices in an effort to achieve improved efficacy, cost–effectiveness, ecological soundness, and sustainability of vector control for malaria An overview of the various ABMs, GIS, and spatial statistical methods used in entomological and epidemiological studies, as well as the model malaria study A companion website with computer source code and flowcharts of the spatial ABM and a landscape generator tool that can simulate landscapes with varying spatial heterogeneity of different types of resources including aquatic habitats and houses Spatial Agent–Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology is an excellent reference for professionals such as modeling and simulation experts, GIS experts, spatial analysts, mathematicians, statisticians, epidemiologists, health policy makers, as well as researchers and scientists who use, manage, or analyze infectious disease data and/or infectious disease–related projects. The book is also ideal for graduate–level courses in modeling and simulation, bioinformatics, biostatistics, public health and policy, and epidemiology. S. M. Niaz Arifin, PhD, is Research Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. A member of The Society for Computer Simulation, and American Society of Tropical Medicine and Hygiene, and the recipient of The American Society of Tropical Medicine and Hygiene Travel Award in 2011, his research interests include agent–based modeling and simulation, public health, data warehousing, and geographic information systems. Gregory R. Madey, PhD, is Research Professor in the Department of Computer Science and Engineering at the University of Notre Dame. A member of The Society for Computer Simulation, Institute of Electrical and Electronics Engineers Computer Society, and American Society of Tropical Medicine and Hygiene, his research interests include agent–based modeling and simulation, cyberinfrastructure, bioinformatics, biocomplexity, e–Technologies, open source software, disaster management, and health informatics. Frank H. Collins, PhD, is Professor in the Department of Biological Sciences at the University of Notre Dame. His research interests include genome level studies of arthropod vectors of human pathogens, the biology of malaria vectors with a focus on the development of molecular tools that will permit better resolution of questions about vector population ec
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